Where Do We Go From Here? A Framework for Using Susceptible-Infectious-Recovered Models for Policy Making in Emerging Infectious Diseases.

OBJECTIVES: Throughout the coronavirus disease 2019 pandemic, susceptible-infectious-recovered (SIR) modeling has been the preeminent modeling method to inform policy making worldwide. Nevertheless, the usefulness of such models has been subject to controversy. An evolution in the epidemiological modeling field is urgently needed, beginning with an agreed-upon set of modeling standards for policy recommendations. The objective of this article is to propose a set of modeling standards to support policy decision making. METHODS: We identify and describe 5 broad standards: transparency, heterogeneity, calibration and validation, cost-benefit analysis, and model obsolescence and recalibration. We give methodological recommendations and provide examples in the literature that employ these standards well. We also develop and demonstrate a modeling practices checklist using existing coronavirus disease 2019 literature that can be employed by readers, authors, and reviewers to evaluate and compare policy modeling literature along our formulated standards. RESULTS: We graded 16 articles using our checklist. On average, the articles met 6.81 of our 19 categories (36.7%). No articles contained any cost-benefit analyses and few were adequately transparent. CONCLUSIONS: There is significant room for improvement in modeling pandemic policy. Issues often arise from a lack of transparency, poor modeling assumptions, lack of a system-wide perspective in modeling, and lack of flexibility in the academic system to rapidly iterate modeling as new information becomes available. In anticipation of future challenges, we encourage the modeling community at large to contribute toward the refinement and consensus of a shared set of standards for infectious disease policy modeling.

Species diversity concurrently dilutes and amplifies transmission in a zoonotic host-pathogen system through competing mechanisms.

In this era of unprecedented biodiversity loss and increased zoonotic disease emergence, it is imperative to understand the effects of biodiversity on zoonotic pathogen dynamics in wildlife. Whether increasing biodiversity should lead to a decrease or increase in infection prevalence, termed the dilution and amplification effects, respectively, has been hotly debated in disease ecology. Sin Nombre hantavirus, which has an ∼35% mortality rate when it spills over into humans, occurs at a lower prevalence in the reservoir host, the North American deermouse, in areas with higher small mammal diversity-a dilution effect. However, the mechanism driving this relationship is not understood. Using a mechanistic mathematical model of infection dynamics and a unique long-term, high-resolution, multisite dataset, it appears that the observed dilution effect is a result of increasing small-mammal diversity leading to decreased deermouse population density and, subsequently, prevalence (a result of density-dependent transmission). However, once density is taken into account, there is an increase in the transmission rate at sites with higher diversity-a component amplification effect. Therefore, dilution and amplification are occurring at the same time in the same host-pathogen system; there is a component amplification effect (increase in transmission rate), but overall a net dilution because the effect of diversity on reservoir host population density is stronger. These results suggest we should focus on how biodiversity affects individual mechanisms that drive prevalence and their relative strengths if we want to make generalizable predictions across host-pathogen systems.

Human ectoparasites and the spread of plague in Europe during the Second Pandemic.

Plague, caused by the bacterium Yersinia pestis, can spread through human populations by multiple transmission pathways. Today, most human plague cases are bubonic, caused by spillover of infected fleas from rodent epizootics, or pneumonic, caused by inhalation of infectious droplets. However, little is known about the historical spread of plague in Europe during the Second Pandemic (14-19th centuries), including the Black Death, which led to high mortality and recurrent epidemics for hundreds of years. Several studies have suggested that human ectoparasite vectors, such as human fleas (Pulex irritans) or body lice (Pediculus humanus humanus), caused the rapidly spreading epidemics. Here, we describe a compartmental model for plague transmission by a human ectoparasite vector. Using Bayesian inference, we found that this model fits mortality curves from nine outbreaks in Europe better than models for pneumonic or rodent transmission. Our results support that human ectoparasites were primary vectors for plague during the Second Pandemic, including the Black Death (1346-1353), ultimately challenging the assumption that plague in Europe was predominantly spread by rats.

Fast transient optimization of social distancing during Covid-19 pandemics using extremum seeking.

In this work, the application of a model-free extremum seeking strategy is investigated to achieve the hypothetical control of the covid-19 pandemics by acting on social distancing. The advantage of this procedure is that it does not rely on the accurate knowledge of an epidemiological model and takes realistic constraints into account, such as hospital capacities. The simulation study reveals that the convergence has two time scales, with a fast catch of the transient optimum of the measurable cost function, followed by a slow tracking of this optimum following the original SIR dynamics. Several issues are discussed such as quantization of the sanitary measures.

Epidemic based modeling of enzymatic hydrolysis of lignocellulosic biomass.

An epidemic based model was developed to describe the enzymatic hydrolysis of a lignocellulosic biomass, dilute sulfuric acid pretreated corn stover. The process of substrate getting adsorbed and digested by enzyme was simulated as susceptibles getting infected by viruses and becoming removed and recovered. This model simplified the dynamic enzyme "infection" process and the catalysis of cellulose into a two-parameter controlled, enzyme behavior guided mechanism. Furthermore, the model incorporates the adsorption block by lignin and inhibition effects on cellulose catalysis. The model satisfactorily predicted the enzyme adsorption and hydrolysis, negative role of lignin, and inhibition effects over hydrolysis for a broad range of substrate and enzyme loadings. Sensitivity analysis was performed to evaluate the incorporation of lignin and other inhibition effects. Our model will be a useful tool for evaluating the effects of parameters during hydrolysis and guide a design strategy for continuous hydrolysis and the associated process control.